CAPTAIN workflow continental vs highseas

Visualise CAPTAIN results

R libraries

library(readr)
library(ggplot2)
library(sf)
library(rnaturalearth)
library(rnaturalearthdata)
library(dplyr)
library(gridExtra)
library(biscale)
library(colorspace)
library(grid)
library(jsonlite)
library(here)

FUSE : conservation prioritiy maps

# Read both RDS files from the Data folder
continental_data <- readRDS(here::here("Data/FUSE_continental_full_results_averaged_budget0.3_replicates10.rds"))
high_seas_data <- readRDS(here::here("Data/FUSE_full_highseas_results_averaged_budget0.3_replicates10.rds"))

# Get world map data
world <- ne_countries(scale = "medium", returnclass = "sf")

# Define the McBryde-Thomas 2 projection
mcbryde_thomas_2 <- "+proj=mbt_s"

# Transform both datasets to sf objects and project
continental_sf <- st_as_sf(continental_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
  st_transform(crs = mcbryde_thomas_2)

high_seas_sf <- st_as_sf(high_seas_data, coords = c("Longitude", "Latitude"), crs = 4326) %>%
  st_transform(crs = mcbryde_thomas_2)

# Combine the datasets
combined_sf <- rbind(
  mutate(continental_sf, Region = "Continental Waters"),
  mutate(high_seas_sf, Region = "High Seas")
)

# Project the world map
world_projected <- st_transform(world, crs = mcbryde_thomas_2)

# Create the globe bounding box
globe_bbox <- rbind(c(-180, -90), c(-180, 90), 
                    c(180, 90), c(180, -90), c(-180, -90))

# Create the globe border
globe_border <- st_polygon(list(globe_bbox)) %>%
  st_sfc(crs = 4326) %>%
  st_sf(data.frame(rgn = 'globe', geom = .)) %>%
  smoothr::densify(max_distance = 0.5) %>%
  st_transform(crs = mcbryde_thomas_2)

# Create base theme
my_theme <- theme_minimal() +
  theme(
    legend.position = "bottom",
    legend.direction = "horizontal",
    legend.box = "vertical",
    legend.margin = margin(t = 20, r = 0, b = 0, l = 0),
    legend.title = element_text(margin = margin(b = 10)),
    panel.background = element_rect(fill = "white", color = NA),
    plot.background = element_rect(fill = "white", color = NA),
    panel.grid = element_blank()
  )

# 1. Continental Waters Plot
continental_plot <- ggplot() +
  geom_sf(data = continental_sf, aes(color = Priority), size = 0.5, alpha = 0.7) +
  geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
  geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
  scale_color_gradientn(
    colors = c("white", "yellow", "darkblue"),
    values = c(0, 0.5, 1),
    name = "Priority",
    guide = guide_colorbar(barwidth = 20, barheight = 0.5, 
                         title.position = "top", title.hjust = 0.5)
  ) +
  labs(title = "Conservation Priorities in Continental Waters",
       subtitle = "Index: FUSE, Budget: 0.3, Replicates: 10",
       x = NULL, y = NULL) +
  my_theme

# 2. High Seas Plot
high_seas_plot <- ggplot() +
  geom_sf(data = high_seas_sf, aes(color = Priority), size = 0.5, alpha = 0.7) +
  geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
  geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
  scale_color_gradientn(
    colors = c("white", "yellow", "darkblue"),
    values = c(0, 0.5, 1),
    name = "Priority",
    guide = guide_colorbar(barwidth = 20, barheight = 0.5, 
                         title.position = "top", title.hjust = 0.5)
  ) +
  labs(title = "Conservation Priorities in High Seas",
       subtitle = "Index: FUSE, Budget: 0.3, Replicates: 10",
       x = NULL, y = NULL) +
  my_theme

# Combined Plot (modified)
combined_plot <- ggplot() +
  geom_sf(data = combined_sf, 
          aes(color = Priority), 
          size = 0.5, 
          alpha = 0.7) +
  geom_sf(data = world_projected, fill = "lightgrey", color = "lightgrey", size = 0.1) +
  geom_sf(data = globe_border, fill = NA, color = "black", size = 0.5) +
  scale_color_gradientn(
    colors = c("white", "yellow", "darkblue"),
    values = c(0, 0.5, 1),
    name = "Priority",
    guide = guide_colorbar(barwidth = 20, barheight = 0.5, 
                         title.position = "top", title.hjust = 0.5)
  ) +
  labs(title = "Combined Conservation Priorities",
       subtitle = "Continental Waters and High Seas\nIndex: FUSE, Budget: 0.3, Replicates: 10",
       x = NULL, y = NULL) +
  my_theme

# Display all three plots
#library(patchwork)
continental_plot 

high_seas_plot 

combined_plot

FUSE : species level priorities

# Protection fraction summary
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_continental.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))

# First, get unique species mappings
species_mapping <- sp_in_data %>%
  distinct(sp, species_name)

# Merge protection fractions with species names
prot_frac_with_names <- prot_frac %>%
  rename(sp = Species_ID) %>%  # rename to match sp_in_data column
  left_join(species_mapping, by = "sp")

# Create species-FUSE mapping using the JSON data
species_FUSE_map <- data.frame(
  Species = sp$FUSE$info$Species,
  FUSE = as.numeric(sp$FUSE$info$FUSE)
)

# Add FUSE scores by species name
prot_frac_complete <- prot_frac_with_names %>%
  left_join(species_FUSE_map, by = c("species_name" = "Species"))

# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac_complete, aes(x = Mean_Protect_Fraction)) +
  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
  scale_x_continuous(limits=c(0,1)) + 
  theme_minimal() +
  labs(title = "Histogram of Mean Protect Fraction\n(Continental)",
       x = "Mean Protect Fraction",
       y = "Count")

# Create histogram for FUSE
hist_fuse <- ggplot(prot_frac_complete, aes(x = FUSE)) +
  geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
  theme_minimal() +
  labs(title = "Histogram of FUSE Scores\n(Continental)",
       x = "FUSE Score",
       y = "Count")

# Create scatterplot
scatter_plot <- ggplot(prot_frac_complete, aes(x = FUSE, y = Mean_Protect_Fraction)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  theme_minimal() +
  scale_y_continuous(limits=c(0,1)) + 
  labs(title = "Scatterplot: FUSE vs Mean Protect Fraction (Continental)",
       x = "FUSE Score",
       y = "Mean Protect Fraction")

# Arrange plots in a grid
grid_plot <- grid.arrange(
  hist_protect, hist_fuse, scatter_plot,
  layout_matrix = rbind(c(1,2), c(3,3)),
  widths = c(1, 1),
  heights = c(1, 1)
)

#High seas waters
# Protection fraction summary for high seas
# Read the data
prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
sp_in_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))

# First, get unique species mappings
species_mapping <- sp_in_data %>%
  distinct(sp, species_name)

# Merge protection fractions with species names
prot_frac_with_names <- prot_frac %>%
  rename(sp = Species_ID) %>%  # rename to match sp_in_data column
  left_join(species_mapping, by = "sp")

# Create species-FUSE mapping using the JSON data
species_FUSE_map <- data.frame(
  Species = sp$FUSE$info$Species,
  FUSE = as.numeric(sp$FUSE$info$FUSE)
)

# Add FUSE scores by species name
prot_frac_complete <- prot_frac_with_names %>%
  left_join(species_FUSE_map, by = c("species_name" = "Species"))

# Create histogram for Mean_Protect_Fraction
hist_protect <- ggplot(prot_frac_complete, aes(x = Mean_Protect_Fraction)) +
  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
  scale_x_continuous(limits=c(0,1)) + 
  theme_minimal() +
  labs(title = "Histogram of Mean Protect Fraction\n(High Seas)",
       x = "Mean Protect Fraction",
       y = "Count")

# Create histogram for FUSE
hist_fuse <- ggplot(prot_frac_complete, aes(x = FUSE)) +
  geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
  theme_minimal() +
  labs(title = "Histogram of FUSE Scores\n(High Seas)",
       x = "FUSE Score",
       y = "Count")

# Create scatterplot
scatter_plot <- ggplot(prot_frac_complete, aes(x = FUSE, y = Mean_Protect_Fraction)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  theme_minimal() +
  scale_y_continuous(limits=c(0,1)) + 
  labs(title = "Scatterplot: FUSE vs Mean Protect Fraction (High Seas)",
       x = "FUSE Score",
       y = "Mean Protect Fraction")

# Arrange plots in a grid
grid_plot <- grid.arrange(
  hist_protect, hist_fuse, scatter_plot,
  layout_matrix = rbind(c(1,2), c(3,3)),
  widths = c(1, 1),
  heights = c(1, 1)
)

#Now combine both and weigth by range size
library(tidyverse)
library(gridExtra)
library(jsonlite)
library(here)

# For the combined analysis part, modify similarly:
continental_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_continental.rds"))
highseas_prot_frac <- readRDS(here::here("Data/protect_fraction_summary_FUSE_03_highseas.rds"))
sp <- fromJSON(here("Data", "shark_conservation_metrics_no_freshwater.json"))
continental_sp_data <- read_csv(here("Data", "continental_puvsp_harmonised.csv"))
highseas_sp_data <- read_csv(here("Data", "highseas_puvsp_harmonised.csv"))

# Get species mappings for both datasets
continental_species_mapping <- continental_sp_data %>%
  distinct(sp, species_name)
highseas_species_mapping <- highseas_sp_data %>%
  distinct(sp, species_name)

# Add species names to both datasets
continental_prot_frac <- continental_prot_frac %>%
  rename(sp = Species_ID) %>%
  left_join(continental_species_mapping, by = "sp")

highseas_prot_frac <- highseas_prot_frac %>%
  rename(sp = Species_ID) %>%
  left_join(highseas_species_mapping, by = "sp")

# Calculate range sizes
continental_ranges <- continental_sp_data %>%
  group_by(sp, species_name) %>%
  summarise(continental_range = n(), .groups = "drop")

highseas_ranges <- highseas_sp_data %>%
  group_by(sp, species_name) %>%
  summarise(highseas_range = n(), .groups = "drop")

# Create species-FUSE mapping
species_FUSE_map <- data.frame(
  Species = sp$FUSE$info$Species,
  FUSE = as.numeric(sp$FUSE$info$FUSE)
)

# Combine the protection fractions with range sizes
combined_protection <- full_join(
  continental_prot_frac %>% 
    select(sp, species_name, Mean_Protect_Fraction) %>%
    rename(continental_protection = Mean_Protect_Fraction),
  highseas_prot_frac %>% 
    select(sp, species_name, Mean_Protect_Fraction) %>%
    rename(highseas_protection = Mean_Protect_Fraction),
  by = c("sp", "species_name")
) %>%
  # Join with the range sizes
  left_join(continental_ranges, by = c("sp", "species_name")) %>%
  left_join(highseas_ranges, by = c("sp", "species_name")) %>%
  left_join(species_FUSE_map, by = c("species_name" = "Species"))

# Calculate weighted protection
combined_protection <- combined_protection %>%
  mutate(
    # Replace NA with 0 for protection values and ranges
    continental_protection = replace_na(continental_protection, 0),
    highseas_protection = replace_na(highseas_protection, 0),
    continental_range = replace_na(continental_range, 0),
    highseas_range = replace_na(highseas_range, 0),
    # Calculate total range
    total_range = continental_range + highseas_range,
    # Calculate weighted protection
    weighted_protection = (continental_protection * continental_range + 
                         highseas_protection * highseas_range) / 
                         total_range
  )

# Add FUSE scores
combined_protection <- left_join(combined_protection, species_FUSE_map, by = c("species_name" = "Species"))

# Create summary statistics
summary_stats <- combined_protection %>%
  select(-species_name) %>%  # Remove the species name column as it's not numerical
  summarise(across(everything(), list(
    min = ~min(., na.rm = TRUE),
    q1 = ~quantile(., 0.25, na.rm = TRUE),
    median = ~median(., na.rm = TRUE),
    mean = ~mean(., na.rm = TRUE),
    q3 = ~quantile(., 0.75, na.rm = TRUE),
    max = ~max(., na.rm = TRUE)
  ))) %>%
  pivot_longer(everything(), 
               names_to = c("variable", "stat"), 
               names_pattern = "(.*)_(.*)") %>%
  pivot_wider(names_from = stat, values_from = value)

# Create and format the flextable
library(flextable)

summary_table <- flextable(summary_stats) %>%
  set_header_labels(
    variable = "Variable",
    min = "Minimum",
    q1 = "1st Quartile",
    median = "Median",
    mean = "Mean",
    q3 = "3rd Quartile",
    max = "Maximum"
  ) %>%
  colformat_double(digits = 3) %>%  # Format numbers to 3 decimal places
  theme_vanilla() %>%
  autofit()

# Display the table
summary_table

Variable	Minimum	1st Quartile	Median	Mean	3rd Quartile	Maximum
sp	1.000	269.000	530.000	534.971	798.000	1,075.000
continental_protection	0.000	0.302	0.308	0.352	0.336	1.000
highseas_protection	0.000	0.000	0.000	0.101	0.000	1.000
continental_range	0.000	56.000	193.000	1,248.928	650.000	40,875.000
highseas_range	0.000	0.000	0.000	805.935	0.000	63,442.000
FUSE.x	0.000	0.000	0.001	0.059	0.031	1.000
total_range	1.000	56.000	200.000	2,054.864	655.000	104,317.000
weighted_protection	0.300	0.303	0.309	0.356	0.341	1.000
FUSE.y	0.000	0.000	0.001	0.059	0.031	1.000

# Create visualizations
hist_protect <- ggplot(combined_protection, aes(x = weighted_protection)) +
  geom_histogram(binwidth = 0.05, fill = "skyblue", color = "black") +
  scale_x_continuous(limits=c(0,1)) + 
  theme_minimal() +
  labs(title = "Histogram of Range-Weighted Protection \nFraction (Combined)",
       x = "Weighted Protection Fraction",
       y = "Count")

hist_fuse <- ggplot(combined_protection, aes(x = FUSE.x)) +
  geom_histogram(binwidth = 0.05, fill = "lightgreen", color = "black") +
  theme_minimal() +
  labs(title = "Histogram of FUSE Scores",
       x = "FUSE Score",
       y = "Count")

scatter_plot <- ggplot(combined_protection, aes(x = FUSE.x, y = weighted_protection)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  theme_minimal() +
  scale_y_continuous(limits=c(0,1)) + 
  labs(title = "Scatterplot: FUSE vs Weighted Protection Fraction (Combined)",
       x = "FUSE Score",
       y = "Weighted Protection Fraction")

# Create species range type summary
range_type_summary <- combined_protection %>%
  summarise(
    total_species = n(),
    continental_only = sum(highseas_range == 0 & continental_range > 0),
    highseas_only = sum(continental_range == 0 & highseas_range > 0),
    both_ranges = sum(continental_range > 0 & highseas_range > 0)
  ) %>%
  pivot_longer(everything(), 
               names_to = "Distribution Type",
               values_to = "Number of Species") 

# Create and format the flextable
range_type_table <- flextable(range_type_summary) %>%
  set_header_labels(
    `Distribution Type` = "Distribution Type",
    `Number of Species` = "Number of Species"
  ) %>%
  theme_vanilla() %>%
  autofit()

# Display the table
range_type_table

Distribution Type	Number of Species
total_species	1,005
continental_only	802
highseas_only	5
both_ranges	198

# Arrange plots in a grid
grid_plot <- grid.arrange(
  hist_protect, hist_fuse, scatter_plot,
  layout_matrix = rbind(c(1,2), c(3,3)),
  widths = c(1, 1),
  heights = c(1, 1)
)

# Save the combined protection data
saveRDS(combined_protection, file = here::here("Data", "combined_protection_analysis.rds"))